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A Secure Mutual Authentication Scheme of Blockchain-Based in WBANs

China Communications 2020年第9期17卷 34-49页

作者： Jianbo Xu Xiangwei Meng Wei Liang Hongbo Zhou Kuan-Ching Li School of Computer science and Engineering Hunan University of Science and TechnologyXiangtan 411201China College of Computer science and Electronic engineering Hunan UniversityChangsha 410082China School of Software Quanzhou University of Information engineeringQuanzhou 362000China Department of Computer Science and Information engineering Taichung 43301TaiwanChina

Wireless Body Area Networks(WBANs)refer to small sensor network that consists of sensor devices mounted on the surface of the body or implanted in the body,as such networks are employed to harvest physiological data of the human body or to act as an assistant regulator of several specific physiological indicators of the human *** sensor devices transmit the harvested human physiological data to the local node via a public *** transmitting data,the sensor device and the local node should perform mutual authentication and key *** is proposed in this paper a secure mutual authentication scheme of blockchain-based in *** analyze the security of this scheme,formal security analysis,and informal security analysis are used,then the computation and communication costs are compared with those of the relevant *** experimental results reveal that the proposed scheme exhibit more effective control over energy consumption and promising.

关键词： information security authentication blockchain WBANs Real-or-Random model

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科学研究的第五范式——以智能驱动的材料设计为例

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engineering 2023年第5期24卷 126-137,I0003,I0004页

作者： Can Leng Zhuo Tang Yi-Ge Zhou Zean Tian Wei-Qing Huang Jie Liu Keqin Li Kenli Li Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense TechnologyChangsha 410073China Laboratory of Software Engineering for Complex Systems National University of Defense TechnologyChangsha 410073China National Supercomputing Center in Changsha Changsha 410082China College of Computer Science and Electronic Engineering Hunan UniversityChangsha 410082China Institute of Chemical Biology and Nanomedicine State Key Laboratory of Chemo/Biosensing and ChemometricsCollege of Chemistry and Chemical EngineeringHunan UniversityChangsha 410082China Department of Applied Physics School of Physics and ElectronicsHunan UniversityChangsha 410082China Department of Computer Science State University of New YorkNew PaltzNY 12561USA

科学正在进入一个新时代——第五范式——它被认为是知识整合到不同领域的主要特征,是基于无所不在的机器学习系统的计算社区中智能驱动的工作。在此,我们通过在天河一号超级计算机系统上构建的催化材料专门设计的典型平台案例,生动地... 详细信息

科学正在进入一个新时代——第五范式——它被认为是知识整合到不同领域的主要特征,是基于无所不在的机器学习系统的计算社区中智能驱动的工作。在此,我们通过在天河一号超级计算机系统上构建的催化材料专门设计的典型平台案例,生动地阐明了第五范式的本质,旨在促进第五范式在其他领域的培养。第五范式平台主要包括模型自动构建(原始数据提取)、指纹自动构建(神经网络特征选择)以及跨学科知识串联的重复迭代(“火山图”)。与分解一起进行的是对迭代中实现的体系结构的性能评估。通过讨论,第五范式的智能驱动平台可以极大地简化和改进研究中极其繁琐和具有挑战性的工作,并通过补偿机器学习中缺少样本和替代一些由于计算资源不足而导致的数值计算来实现数值计算与机器学习之间的相互反馈,从而加速探索过程。在数据驱动的学科中,跨学科专家的协同作用和对动态数据需求的急剧增长仍然是一个挑战。我们相信,对第五范式平台的一瞥可以为其在其他领域的应用铺平道路。

关键词：机器学习自动构建天河一号数据驱动神经网络动态数据知识整合科学研究

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Delving into the Reversal Curse: How Far Can Large Language Models Generalize?

arXiv

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arXiv 2024年

作者： Lin, Zhengkai Fu, Zhihang Liu, Kai Xie, Liang Lin, Binbin Wang, Wenxiao Cai, Deng Wu, Yue Ye, Jieping State Key Lab of CAD&CG Zhejiang University China Alibaba Cloud College of Biomedical Engineering & Instrument Science Zhejiang University China College of Computer Science and Technology Zhejiang University of Technology China School of Software Technology Zhejiang University China Fullong Inc China

While large language models (LLMs) showcase unprecedented capabilities, they also exhibit certain inherent limitations when facing seemingly trivial tasks. A prime example is the recently debated "reversal curse", which surfaces when models, having been trained on the fact "A is B", struggle to generalize this knowledge to infer that "B is A". In this paper, we examine the manifestation of the reversal curse across various tasks and delve into both the generalization abilities and the problem-solving mechanisms of LLMs. This investigation leads to a series of significant insights: (1) LLMs are able to generalize to "B is A" when both A and B are presented in the context as in the case of a multiple-choice question. (2) This generalization ability is highly correlated to the structure of the fact "A is B" in the training documents. For example, this generalization only applies to biographies structured in "[Name] is [Description]" but not to "[Description] is [Name]". (3) We propose and verify the hypothesis that LLMs possess an inherent bias in fact recalling during knowledge application, which explains and underscores the importance of the document structure to successful learning. (4) The negative impact of this bias on the downstream performance of LLMs can hardly be mitigated through training alone. These findings offer a novel perspective on interpreting LLMs' generalization through their intrinsic mechanisms and provide insights for developing more effective learning methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Data description

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A Consumer Electronics-Enhanced UAV System for Agricultural Farm Tracking With Fuzzy SMO and Actuator Fault Detection Control Algorithms

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IEEE Transactions on Consumer Electronics 2025年

作者： Bilal, Hazrat Aslam, Muhammad Shamrooz Tian, Yibin Ullah, Inam Ayouni, Sarra Vasilakos, Athanasios V. College of Mechatronics and Control Engineering College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China China University of Mining and Technology Artificial Intelligence Research Institute Xuzhou2211106 China Shenzhen University College of Mechatronics and Control Engineering Shenzhen518060 China Gachon University Department of Computer Engineering Seongnam13120 Korea Republic of Tashkent State University of Economics Department of Artificial Intelligence Tashkent100066 Uzbekistan Princess Nourah bint Abdulrahman University Department of Information Systems College of Computer and Information Sciences Saudi Arabia Grimstad Norway

The adoption of agricultural robots, or agrobots, has revolutionized modern farming operations, ranging from crop monitoring to automated harvesting, significantly boosting productivity. Motivated by the rapid advancements in agrobots and their integration into smart agricultural practices, this study proposes an autonomous trajectory tracking system for wheat farms using quadcopter UAVs. To address actuator fault detection, including stuck faults and partial loss of efficiency, a TSF-H∞-SMO (Takagi-Sugeno Fuzzy-based H∞ Sliding Mode Observer) fault detection framework is introduced. The approach initiates with the derivation of a TSF (Takagi-Sugeno Fuzzy) attitude control model that integrates an uncertainty term, constructed from the original nonlinear dynamics of the UAV and approximated through local linear models at four equilibrium positions. An actuator fault model is subsequently integrated to develop a comprehensive TSF-UAV model, accounting for actuator faults. The TSF-H∞-SMO is then designed using matrix coordinate transformation to enable precise fault detection. The fault detection capabilities of the TSF-H∞-SMO are evaluated through simulations on the TSF-UAV model under SISO (single-input single-output) actuator fault scenarios. The experimental results validate the proposed system, demonstrating its ability to detect a range of actuator faults accurately and promptly. The analysis reveals a proportional relationship between the amplitude of the state change and the severity of the fault, attributed to the interaction between system states and actuator flaps. This approach underscores the potential for deploying autonomous UAV-based fault detection and trajectory tracking systems in agricultural applications. Furthermore, integrating such advanced fault-tolerant control algorithms holds promise for consumer technology applications, where precision, reliability, and robustness are critical to enhancing system performance and operational efficiency. © 1

关键词： Agricultural robots

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PMMVar: Leveraging Multi-level Protein Structures for Enhanced Coding Variant Pathogenicity Prediction

PMMVar: Leveraging Multi-level Protein Structures for Enhanc...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yijia Chen Yiwen Chen Shanling Nie Hai Yang Department of Computer Science and Engineering East China University of Science and Technology Shanghai China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai China Center for Continuing and Lifelong Education National University of Singapore Singapore Faculty of Engineering The University of Sydney Australia

Genomic variants, which can disrupt cellular functions, present a challenge in distinguishing deleterious from benign instances. While assessing genome-wide functional impacts, many current algorithms neglect protein tertiary structure of coding region variants due to limitations in protein structural prediction. This study introduces PMMVar, an advanced multimodal deep convolutional network, which adeptly integrates protein tertiary structures with conservation properties from ESM-2, supplemented by other protein structural sequences. PMMVar achieves outstanding performance on the latest clinical variant datasets, NCBI ClinVar (2023), and the Mendelian variant dataset, surpassing existing benchmarks. Ablation analyses validate the significance of protein multi-level structures in enhancing the model’s accuracy. Overall, our findings spotlight the essential role of multi-level protein structures in pathogenicity predictions and their potential to discern deleterious genomic variants effectively.

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Pointsurface: Discriminative Point Cloud Surface Feature Extraction for 3d Face Recognition

SSRN

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SSRN 2024年

作者： Yang, Junpeng Li, Qiufu Shen, Linlin Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University Guangdong Shenzhen518060 China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Guangdong Shenzhen518060 China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen University Guangdong Shenzhen518172 China

3D face recognition performs better than 2D face recognition, in terms of robustness against lighting and digital attacks. Compared to 2D data, the rich geometric information in 3D data could be very useful to improve the performance of 3D face recognition. This paper introduces PointSurFace, a surface feature extraction integrated model tailored for point cloud based 3D face recognition. PointSurFace adopts a specially designed surface feature extraction module, which explores information about the position and normals of the 3D face surface to explicitly extract local discriminative features from 3D point cloud faces. Subsequently, it alternates between set abstraction (SA) layers and Inverted Residual MLP (InvResMLP) modules to effectively extract features from the point cloud data. To better handle the spatial relationships in the point cloud data, PointSurFace integrates coordinate and feature data within each SA layer and InvResMLP module. It also applies channel fusion operations to align these different types of data, thereby more effectively extracts point cloud features. Experimental results in 3D facial recognition and verification across multiple datasets demonstrate that PointSurFace achieves the best 3D face recognition performance to date. For example, on Lock3DFace, it achieves the state-of-the-art recognition and verification accuracy of 90.03% and 80.01%, respectively, surpassing previous methods by 2.02% and 3.19%. Results on cross-quality datasets also affirm PointSurFace’s strong generalization capabilities. Ablation experiments validate the effectiveness of each module within PointSurFace, which suggests that the proposed surface feature extraction module significantly enhances the point cloud based 3D facial recognition performance. © 2024, The Authors. All rights reserved.

关键词： Feature extraction

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Hybrid Deep Learning Algorithm For MRI Brain Alzheimer’s Disease Prediction

Hybrid Deep Learning Algorithm For MRI Brain Alzheimer’s Di...

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Energy, Materials and Communication engineering (ICEMCE), International Conference on

作者： D. Karthikeyan R. Nivasini Kiruba Thangam Raja D. Madhivadhani B. Sivakumar A.T.R. Krishna Priya Department of Electrical and Electronics Engineering SRM Institute of Science and Technology Kattankulathur Chennai Guvi Geek Networks IITM Research Park Chennai Tamilnadu India Department of Software and Systems Engineering School of Computer Science Engineering and Information Systems VIT University Vellore India Department of Electronics and Communication Engineering Kings Engineering College Sriperumbudur Chennai Department of Computer Science and Engineering Rohini College of Engineering and Technology Kanyakumari India

Magnetic resonance imaging (MRI) has been used to study the structural makeup of the brain and analyse several neurological disorders and diseased areas. For the adoption of preventative measures, early recognition of Alzheimer’s disease (AD) patients is essential. Here, a thorough inspection of the tissue arrangements obtained by MRI images of Outcome and Assessment Information (OASIS) dataset enables an exact characterization of certain brain diseases. There have been a number of division techniques for diagnosing AD that range in complexity. Compassion has been tested by deep learning techniques used to segment the structure of the brain and classify AD because they have the potential to uncover important information from vast amounts of data. In this paper the deep learning technique of Hybrid Dragonfly based GWO convolutional Neural Network (CNN) is achieved promising result for the diagnosis of AD. At image preprocessing wiener filter is used for removing the additive noises and the Gray Level Co-Occurrence Matrix (GLCM) extraction is implemented for texture analyzing. As a result, hybrid deep learning methods of CNN has the accuracy as 90% and the result of the image prediction are presented in this paper.

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Protein-Ligand Binding Affinity Prediction Using Deep Learning 18

Protein-Ligand Binding Affinity Prediction Using Deep Learni...

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18th International computer Conference on Wavelet Active Media technology and Information Processing, ICCWAMTIP 2021

作者： Atwereboannah, Abena Achiaa Wu, Wei-Ping Ding, Lei Yussif, Sophyani B. Tenagyei, Edwin Kwadwo University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu China SipingSoft Co. Ltd. Tianfu Software Park Chengdu China

ISBN: (纸本)9781665413640

Protein-ligand prediction plays a key role in drug discovery. Nevertheless, many algorithms are over reliant on 3D structure representations of proteins and ligands which are often rare. Techniques that can leverage the sequence-level representations of proteins, ligands and pockets are thus required to predict binding affinity and facilitate the drug discovery process. We have proposed a deep learning model with an attention mechanism to predict protein-ligand binding affinity. Our model is able to make comparable achievements with state-of-the-art deep learning models used for protein-ligand binding affinity prediction. © 2021 IEEE.

关键词： Forecasting

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Smart Farmer Education System for Disease Identification, Severity Assessment and Infestation Management (Brinjal)

Smart Farmer Education System for Disease Identification, Se...

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International Conference on Industrial and Information Systems, ICIIS

作者： Keshara Bandara Samitha Vidhanaarachchi A.A.E. Nanayakkara Kavindu Kulasinghe Sanka Rajapakshe Asela Wickramasinghe Kanishka Yapa Department Of Information Technology Sri Lanka Institute Of Information Technology Malabe Sri Lanka Department Of Computer Science and software Engineering Sri Lanka Institute Of Information Technology Malabe Sri Lanka Department of Soil Science Faculty of Agriculture University of Ruhuna Mapalana Kamburupitiya Sri Lanka Department of Computer Systems Engineering Sri Lanka Institute Of Information Technology Malabe Sri Lanka

The agriculture industry is one of the most significant sources of foreign exchange and employment in the Sri Lankan market. Therefore, small crops play a crucial role in ensuring the food security of the population as they are integral components of Sri Lankan cuisine. However, industry experts have identified inefficient disease and pest management as a hindrance to production. This issue requires immediate attention and proper action, which can be effectively addressed through the utilization of ICT technologies. Consequently, this research paper proposes a smart system that aims to assist farmers and agriculture professionals by providing them with farmer education, disease and infestation management tools, as well as progression level calculations. The implementation of this system aims to uplift the agriculture industry in Sri Lanka. The developed ‘AI-based mobile application system for Brinjal Diseases,’ which is based on analyzing the eating patterns of leafhopper damage, presents an innovative approach to identify the most significant threats to Brinjal, specifically focusing on leafhopper damage. This proposed system effectively informs various stakeholders, including the Area Agricultural Research Officer, Seed Certification and Plant Protection Center (SCPPC), Agricultural Service Center, Office of the Register of Pesticides (ROP), Plant Protection Service (PPS), as well as farmers, regarding the dispersion and prevention of brinjal diseases and pest infections.

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Hcl-Classifier: Cnn and Lstm Based Hybrid Malware Classifier for Internet of Things(Iot)

SSRN

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SSRN 2022年

作者： Abdullah, Muhammed Amin Yu, Yongbin Adu, Kwabena Imrana, Yakubu Wang, Xiangxiang Jingye, Cai School of Information and Software Engineering University of Electronic Science and Technology of China Sichuan Province China School of Computer Science and Engineering University of Electronic Science and Technology of China China

This paper highlights a hybrid static classifier based on CNN and bi-directional LSTM for malware classification tasks in the IoT. Our approach learns and takes note of the nature and complex patterns of the Byte and Assemble files represented in one-dimensional images for better feature extraction. CNN is employed for automatic feature selection and extraction. Furthermore, extracted features are forwarded to the bi-directional LSTM for classification. Extensive experiments were performed using the Microsoft malware classification datasets. Experimental results show that our HCL-Classifier achieves 99.91%, thus 0.5% better than the state-of-the-artworks. Moreover, the least performed classifier among the base models, such as the random forest, used in this paper achieved an accuracy of 98.34%. We attribute this to the nature of our 1D image representation approach. This study also discovered that different files in the dataset contain specific features that differ from file to file, and this we proved visually and through experiment © 2022, The Authors. All rights reserved.

关键词： Internet of things

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